Related Experiment Videos
A conservative parametric approach to motif significance analysis
1Department of Computer Science, Cornell University, Ithaca, NY, USA.
Summary
This study introduces a new parametric method to assess motif finder significance. It uses a 3-parameter Gamma distribution fit for motif scores, enabling reliable p-value confidence intervals for various biological sequence analysis tools.
Area of Science:
- Bioinformatics
- Computational Biology
- Statistical Genomics
Background:
- Motif discovery is crucial for understanding gene regulation.
- Assessing the statistical significance of discovered motifs is challenging.
- Existing methods may lack robustness across different tools and datasets.
Purpose of the Study:
- To develop a novel, parametric approach for estimating motif finder output significance.
- To provide a reliable method for calculating p-value confidence intervals for motif scores.
- To establish a versatile method applicable to various motif discovery tools.
Main Methods:
- Utilizing a 3-parameter Gamma distribution family.
- Modeling the null distribution of motif scores.
- Computing conservative confidence intervals for p-values.
Main Results:
- Demonstrated a good fit of the 3-parameter Gamma distribution to motif score null distributions.
- Showcased the utility of the derived confidence intervals for p-values.
- Validated the approach across multiple motif finders, background models, and scoring functions.
Conclusions:
- The proposed parametric approach offers a robust method for motif significance estimation.
- The Gamma distribution fit provides a reliable basis for p-value assessment.
- This method is broadly applicable to diverse motif discovery algorithms in bioinformatics.
Related Concept Videos
01:04Significance Testing: Overview
11.3K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
11.3K
01:49Conserved Binding Sites
5.0K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
5.0K
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
404
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
404
01:12Statistical Methods to Analyze Parametric Data: ANOVA
1.4K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
1.4K
01:50Statistical Significance
21.0K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
21.0K
01:16Critical Region, Critical Values and Significance Level
13.1K
The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
13.1K